Baseline Counterparts Research Articles

Improving the performance of industrial TOPCon solar cells through the insertion of intrinsic a-Si layer

Passivating contact solar cells have gradually become the mainstream cell technology due to their excellent performance, and further improving the conversion efficiency has become a focus of subsequent research. Typically, achieving excellent field-effect passivation and low contact resistivity in doped polycrystalline silicon (poly-Si) requires heavy phosphorus doping. However, this approach can lead to a predicament wherein excessive phosphorus diffuses into the silicon (Si) substrate during annealing, consequently causing recombination losses. In response to this challenge, a structure incorporating an intrinsic amorphous silicon (a-Si (i)) layer within the passivation layers has been introduced. The primary objective of this structure is to retard the diffusion of phosphorus into the Si substrate. This study entails comprehensive characterizations to delve into the underlying mechanisms of films with the integrated a-Si (i) layer, including surface microscopy, active dopants profile, crystallographic structure, elemental distribution, and electrical properties. Finally, we have fabricated the industrial-sized TOPCon solar cells with an average efficiency of 23.83 %, which is 0.25 % higher than that of Baseline counterparts (23.58 %) on the production line. The above results have demonstrated the introduction of a-Si (i) film can be a buffer layer, retarding the diffusion of phosphorus into the Si substrate and obtaining a better passivation effect, enabling us to further tailor the doping profile for high-efficiency solar cells. Our work highlights a promising strategy to improve the performance of TOPCon solar cells, showcasing the substantial potential for implementation in industrial manufacturing.

Bootstrap-quantile ridge estimator for linear regression with applications.

Bootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the analysis of data where predictors are collinear. This paper develops a nonparametric bootstrap-quantile approach for the estimation of ridge parameter in the linear regression model. The proposed method is illustrated using some popular and widely used ridge estimators, but this idea can be extended to any ridge estimator. Monte Carlo simulations are carried out to compare the performance of the proposed estimators with their baseline counterparts. It is demonstrated empirically that MSE obtained from our suggested bootstrap-quantile approach are substantially smaller than their baseline estimators especially when collinearity is high. Application to real data sets reveals the suitability of the idea.

Sentiment analysis of tweets using text and graph multi-views learning

With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity, under-specificity, noise, and multilingual content) through text and network-based representation learning approaches. However, limited studies on combining the benefits of textual and structural (graph) representations for sentiment analysis of tweets have been carried out. This study proposes a multi-view learning framework (end-to-end and ensemble-based) that leverages both text-based and graph-based representation learning approaches to enrich the tweet representation for sentiment classification. The efficacy of the proposed framework is evaluated over three datasets using suitable baseline counterparts. From various experimental studies, it is observed that combining both textual and structural views can achieve better performance of sentiment classification tasks than its counterparts.

Cost-efficient fixed-width confidence intervals for the difference of two Bernoulli proportions

ABSTRACT We study the properties of confidence intervals (CIs) for the difference of two Bernoulli distributions’ success parameters, , in the case where the goal is to obtain a CI of a given half-width while minimising sampling costs when the observation costs may be different between the two distributions. We propose three different methods for constructing fixed-width CIs: (i) a two-stage sampling procedure, (ii) a sequential method that carries out sampling in batches, and (iii) an -stage “look-ahead” procedure. Under diverse scenarios, our proposed algorithms obtain significant cost savings versus their baseline counterparts. Furthermore, for the scenarios under study, our sequential-batches and -stage “look-ahead” procedures approximately obtain the nominal coverage while meeting the desired width requirement. Our sequential-batching method is more efficient than the “look-ahead” method computationally, with average running times an order-of-magnitude faster over the scenarios tested. We illustrate our procedures on a case study comparing generic and brand-name drugs.

Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

A study on corpus-based stopword lists in Indian language IR

We explore and evaluate the effect of different stopword lists (non-corpus-based and corpus-based) in the information retrieval (IR) tasks with different Indian languages such as Bengali, Marathi, Gujarati, Hindi and English. The issue was investigated from three viewpoints. Is there any performance difference between non-corpus-based and corpus-based stopword removal in chosen Indian languages? Can corpus-based stopword lists improve performance in Indian languages IR? If yes, to what extent? Among the different corpus-based stopword lists, which stopword list provides the best IR performance? Does the length of a corpus-based stopword list affect the retrieval performance in Indian languages? If yes, to what extent? It was observed that a corpus-based stopword list provides better retrieval performance than a non-corpus-based stopword list in different Indian languages. Among the different corpus-based stopword lists generated and experimented with, Zipf’s law-based stopword list (idf-based one) provides the best retrieval performance in various Indian languages. The aggregation1-based stopword list provides better retrieval than the aggregation2-based list in Indian languages, but in English, the aggregation2-based stopword list performs better than the aggregation1-based list. The best performing idf-based stopword list improves MAP score by 5.43% in Bengali, 1.91% in Marathi, 5.4% in Gujarati, 1.5% in Hindi and 2.12% in English, respectively, over their baseline counterparts. The probabilistic retrieval models (BM25 and TF-IDF) perform best in different Indian languages. A smaller length of corpus-based stopword lists performs better than a larger length of non-corpus-based stopword lists for all the Indian languages considered. The proposed schemes demonstrate that a stopword list can be heuristically generated in a language-independent statistical method and effectively used for IR tasks with performance comparable, to or even better than non-corpus-based stopword lists.

A single bout of passive exercise mitigates a mental fatigue-induced inhibitory control deficit.

Sustained cognitive effort associated with the psychomotor vigilance task (PVT) increases objective and subjective measures of mental fatigue and elicits a post-PVT inhibitory control deficit. In contrast, passive exercise wherein an individual's limbs are moved via an external force (i.e., mechanically driven cycle ergometer flywheel) provides a postexercise inhibitory control benefit linked to an exercise-based increase in cerebral blood flow. Here, we examined whether passive exercise performed concurrently with the PVT 'blunts' an inhibitory control deficit. On separate days, participants (N = 27) completed a 20min PVT protocol (control condition) and same duration PVT protocol paired with passive cycle ergometry (passive exercise condition). Prior to (i.e., baseline), immediately after and 30min after each condition inhibitory control was assessed via the antisaccade task. Antisaccades require a goal-directed eye movement (i.e., saccade) mirror-symmetrical to a target and provide an ideal tool for evaluating task-based changes in inhibitory control. PVT results showed that vigilance (as assessed via reaction time: RT) during control and passive exercise conditions decreased from the first to last 5min of the protocol and increased subjective ratings of mental fatigue. As well, in the control condition, immediate (but not 30-min) post-intervention antisaccade RTs were longer than their baseline counterparts-a result evincing a transient mental fatigue-based inhibitory control deficit. For the passive exercise condition, immediate and 30-min post-intervention antisaccade RTs were shorter than their baseline counterparts and this result was linked to decreased subjective ratings of mental fatigue. Thus, passive exercise ameliorated the selective inhibitory control deficit associated with PVT-induced mental fatigue and thus provides a potential framework to reduce executive dysfunction in vigilance-demanding occupations.

FuzzyAct: A Fuzzy-Based Framework for Temporal Activity Recognition in IoT Applications Using RNN and 3D-DWT

Despite massive research in deep learning, the human activity recognition (HAR) domain still suffers from key challenges in terms of accurate classification and detection. The core idea behind recognizing activities accurately is to assist Internet-of-things (IoT) enabled smart surveillance systems. Thereby, this work is based on the joint use of discrete wavelet transform (DWT) and recurrent neural network (RNN) to classify and detect human activities accurately. Recent approaches on HAR exploit the three-dimensional (3-D) convolutional neural networks (CNNs) to extract spatial information, which adds a computational burden. In our case, features are extracted using 3D-DWT instead of 3-D CNNs, performed in three steps of 1D-DWT to reflect the spatio-temporal features of human action. Given the features, the RNN produces an output label for each video clip taking care of the long-term temporal consistency among close predictions in the output sequence. It is noticed that feature extraction through 3D-DWT essentially recovers the multiple angles of an activity. Many HAR techniques distinguish an activity based on the posture of an image frame rather than learning the transitional relationship between postures in the temporal sequence, resulting in degraded accuracy. To address this problem, in this article, we designed a novel rank-based fuzzy approach that segregates activities precisely by ranking the probabilities of activities based on confidence scores. FuzzyAct achieved an average mean average precision (mAP) of 0.8012 mAP on the ActivityNet dataset, and outperformed the baseline counterparts and other state-of-the-art approaches on benchmark datasets. Finally, we present a mechanism to compress the proposed RNN for edge-enabled IoT applications.

Dynamic response of Advanced Placed Ply composites

This work investigates the high strain rate behavior of AP-PLY composites. The large representative volume elements and brittle nature of this material necessitated the use of a bespoke Split-Hopkinson bar apparatus. AP-PLY and baseline laminates were subjected to tensile loading at strain rates of 30 s−1. Results were compared with quasi-static data to evaluate whether the laminate architecture introduced any strain rate dependency. In addition, the dynamic experiments were simulated using a multiscale modeling framework, providing further insights into the micromechanisms governing material behavior. The moduli of the AP-PLY composites were found to be strain rate independent, however, strengths were found to be marginally higher than those of their baseline counterparts. At high strain rates, the strain concentrations induced by the geometry of the individual tapes at through thickness undulations and tow boundaries were less significant due to reduced out-of-plane tow straightening and delamination. As a result, no reduction in AP-PLY strength in comparison to the baseline laminates was obtained. These differences in deformation micromechanisms led to an improvement of the damage tolerance when subjected to dynamic loading.

Multi-Location Emulation of a Process-Based Salinity Model Using Machine Learning

Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used process model, the Delta Simulation Model II (DSM2), used for salinity estimation in California’s Sacramento-San Joaquin Delta (Delta). We apply historical daily input data to DSM2 and corresponding salinity simulations at 28 study locations from 1990 to 2019 to train two machine-learning models: a multi-layer perceptron (MLP) and Long-Short-Term Memory (LSTM) networks in a multi-task learning framework. We assess sensitivity of both networks to the amount of antecedent input information (memory) and training data to determine appropriate memory size and training data length. We evaluate network performance according to several statistical metrics as well as visual inspection. The study further investigates two additional networks, the Gated Recurrent Unit (GRU) and Residual Network (ResNet) in salinity modeling, and compares their efficacy against MLP and LSTM. Our results demonstrate strong performance of the four neural network models over the study period, achieving absolute bias below 4%, plus near-perfect correlation coefficients and Nash–Sutcliffe efficiency coefficients. The high complexity LSTM shows slight performance edge. We further show that deeper and wider versions of MLP and LSTM yield only marginal benefit over their baseline counterparts. We also examined issues related to potential overfitting by the proposed models, training data selection strategies, and analytical and practical implications. Overall, this new study indicates that machine-learning-based emulators can efficiently emulate DSM2 in salinity simulation. They exhibit strong potential to supplement DSM2 in salinity modeling and help guide water resource planning and management practices for the Delta region.

Systemic glucocorticoid-free therapy with adalimumab plus immunosuppressants versus conventional therapy in treatment-naïve Vogt-Koyanagi-Harada disease patients.

BackgroundHigh dose systemic glucocorticoid is the main therapy of treatment-naïve Vogt-Koyanagi-Harada (VKH) disease. However, series side effects induced by high dose systemic glucocorticoid frequently occur, which makes alternative therapy necessary for certain patients. This study sought to compare the efficacy and safety of systemic glucocorticoid-free (SGF) therapy with conventional therapy (CT) as an initial treatment for VKH patients.MethodsVKH patients who had not been systemically treated were enrolled. Patients were allocated into 2 therapeutic groups depending on their treatments. In CT group, patients received systemic glucocorticoid plus immunosuppressants (IS), and in SGF group, patients received adalimumab (ADA) plus IS. Patients received approximately 12 months treatment and visit monthly. The outcome parameters included the changes of best-corrected visual acuity (BCVA), intraocular inflammation (including anterior chamber cell grade and vitritis grade) and central macular thickness (CMT) (the change values define as the final-visit values subtracted from baseline counterparts). Other outcomes included the relapses times of ocular inflammation, adverse events (AEs), changes of optic nerve inflammation (ONI) and intraocular/extraocular manifestations.ResultsA total of 30 patients (60 eyes) were included. with 19 patients (38 eyes) in CT group and 11 patients (22 eyes) in SGF group. After approximately 1 year of treatment, the improvements of BCVA were slight better in the SGF group (0.57±0.23) than in the CT group (0.40±0.26), (P=0.014). In both groups, the ocular inflammatory improvements in both groups were similar, with an improvement of AC cell grade of −1.5 (−2, −0.5) in CT group versus −1 (−2, −1) in SGF group (P=0.367); improvement of vitritis grade was 0 (−1.25, 0) in CT group and −1 (−1, −1) in SGF group (P=0.050). The improvement in CMT was similar in both groups, with −523.47±412.09 µm in CT group and −362.73±375.73 µm in SGF group (P=0.572). The mean number of relapses was 1 (0, 2) in the CT group and 0 (0, 2) in the SGF group (P=0.372). No severe AEs were observed in this study.ConclusionsSGF therapy is effective, safe, and well-tolerated in treatment-naïve VKH patients. SGF therapy seems to be a feasible option in patients with existing systemic diseases intolerant to glucocorticoid.

Grassroots sexual and reproductive rights interventions on sexual risk behavior among female sex workers in Nigeria

Introduction: Sexual reproductive health and rights (SRHR) involve access to accurate information, rights-based quality, safe, effective, affordable, and acceptable health-care services at all levels that cover the sexual and reproductive needs of the individual and community irrespective of social, sex, gender, and other differences. Objective: This study aims at assessing the grassroots interventions on the knowledge of rights, access, and uptake of sexual reproductive health (SRH) services among female sex workers (FSWs) in Benue State, Nigeria. Materials and Methods: This cross-sectional survey is a randomized cluster sampling among FSWs in Benue State, Nigeria. A structured questionnaire was used to collect data from the respondents, and the data collected were analyzed using IBM-SPSS Corp., Armonk, NY, version 25.0. The statistical significance level was set at P < 0.05. Results: Respondents were 446 FSWs aged 15–45 years, comprising 223 FSWs at baseline and post-intervention. Significantly higher intervention respondents (84.4%) use condoms while drunk compared with 56.1% at the baseline (P < 0.0001). Overall, 70.0% used lubricants in the past 12 months in the post-intervention study, whereas only 47.5% used them in the baseline study (P < 0.0001). Generally, 21.1% indulged in anal sex, and there was no significant difference between the baseline and intervention (P > 0.05). Virtually all respondents (96.0%) used any contraceptive methods in the intervention period compared with 68.6% at the baseline (P < 0.001). The intervention respondents were 11.84 (95% CI: 4.98–28.16; P < 0.0001) times more likely to use any contraceptive method than their baseline counterparts. The intervention respondents were 2.22 (95% CI: 1.51–3.27; P < 0.0001) times more likely to use emergency contraceptives than their baseline counterparts. Conclusion: This study showed the positive outcomes of grassroots interventions involving FSWs in Benue State, Nigeria. Such interventions should be extended to other key populations in Nigeria.

SwitchNet: Learning to switch for word-level language identification in code-mixed social media text

AbstractWord-level language identification is an essential prerequisite for extracting useful information from code-mixed social media content. Previous studies in word-level language identification show two important observations. First, the local context is an important indicator of the language of a word when a word is valid in multiple languages. Second, considering the word in isolation from its context leads to more effective language classification when a word is borrowed or embedded into sentences of other languages. In this paper, we propose a framework for language identification that makes use of a dynamic switching mechanism for effective language classification of both words that are borrowed or embedded from other languages as well as words that are valid in multiple languages. For a given input, the proposed switching mechanism makes a dynamic decision to bias its prediction either towards the prediction obtained by the contextual information or that obtained by the word in isolation. In contrast to existing studies that rely upon large amounts of annotated data for robust performance in a multilingual environment, the proposed approach uses minimal annotated resources and no external resources, making it easily extendible to newer languages. Evaluation over a corpus of transliterated Facebook comments shows that the proposed approach outperforms its baseline counterparts: classification based on the contextual information, classification based on the word in isolation, as well as an ensemble of the two classifiers.

Neutrophil-lymphocyte score: A novel prognostic scoring system that utilized the dynamic change of neutrophil, lymphocyte, and albumin and its comparison to other indices.

3048 Background: Indications for immune checkpoint inhibitor (ICI) in cancer care are expanding rapidly. There is increasing need for accurate decision tool to better guide treatment. We have constructed a new prognostic scoring system, neutrophil-lymphocyte score (NRS), based on the nonlinear dynamic change of neutrophil to lymphocyte ratio (NLR) in relation to survival over the first cycle of ICI treatment. We compared this novel system to existing indices such as NLR, lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR), Advanced Lung Cancer Inflammation Index (ALI), and Systemic Immune-inflammation Index (SII). Methods: This is a retrospective analysis of 837 patients at Ohio State University from 2011-18. Neutrophil (ANC), lymphocyte (ALC), platelet (plt), monocyte (AMC), albumin (alb), and body mass index (BMI) were collected at baseline. Repeat labs were collected at cycle 2. NLR = ANC/ALC, ALI = BMI x alb / NLR, LMR = ALC/AMC, SII = platelet x NLR, PLR = plt/ALC. NLR Ratio = baseline NLR / repeat NLR. Based on the association between NLR and the overall survival, we assigned 1 point (p) for baseline NLR < 0.7, 6p for 0.7 to < 2, 5p for 2 to < 3, 4 p for 3 to < 4, 3 for 4 to 5, 2p for 5 to < 9, and 1p for ≥9. We also assigned 1p for NLR ratio < 0.6, 2p for 0.6 to < 0.8, 3p for 0.8 to < 1.2, 5p for 1.25 to < 1.4, 3p for 1.4 to < 1.6, and 2p for ≥1.6. NLS = sum of these 2 scores . NLS_A = NLS*alb. Time-dependent receiver operator characteristic (ROC) curves with integrated time-dependent area under the curve (TD AUC) values were used to evaluate the predictive accuracy of each index for survival. Results: For baseline and repeat values, all indices were statistically significant (P < 0.001) in predicting survival. Baseline integrated TD AUC were: ALI 0.704, NLR 0.692, SII 0.663, LMR 0.645, and PLR 0.612. All of the repeat indices at cycle 2 had higher prognostic value than their baseline counterparts. Integrated TD AUC for indices at cycle 2 were: ALI 0.740 (with baseline BMI), NLR 0.729, SII 0.694, LMR 0.671, and PLR 0.652. NLS_A was a composite score based on the dynamic change of NLR from cycle 1 to 2 and the treatment alb with integrated TD-AUC at 0.754. Conclusions: Indices constructed from ANC, ALC, AMC, Plt, alb, and BMI can be obtained inexpensively and provide great prognostic value for pts on ICI. We have constructed a novel scoring system (NLS_A) and demonstrated its improvement over the current prognostic indices. Studies with a larger cohort are needed to further improve and validate this system.

Aerodynamics of ducted re-entry vehicles

Ballistic parameter plays a major role in determining the re-entry trajectory. Lower ballistic coefficient offers an optimal re-entry, wherein the vehicle decelerates higher up in the atmosphere thereby decreasing the imposed aerothermal loads. The current computational study proposes an add-on, to the existing Orion-based re-entry vehicle: a duct circumventing the capsule from the shoulder to the base, to improve the aerocapture ability of the re-entry vehicle. The design cases are categorised based on a non-dimensional parameter termed the Annular Area Ratio (AAR). Dragand ballistic coefficient of the Ducted Re-entry Vehicles (DRVs) at various Mach numbers are evaluated and compared with those of the baseline model. The results show that the proposed design increases the drag for all the AARs considered in the subsonic regime. In the supersonic regime, ducted models of higher AAR are more promising with the increase in Mach number. DRVs also exhibit lower ballistic coefficients than their baseline counterparts.

Fast Recursive Nonnegative Standard and Hierarchical Tucker Decomposition

This letter proposes a new approach to nonnegative Tucker decomposition, which assumes recursive updates of latent factors with any nonnegative matrix factorization algorithm. The proposed strategy is extended to the nonnegatively constrained hierarchical Tucker decomposition model. Numerical experiments confirmed that the proposed algorithms have lower computational complexities and demonstrate improved performance compared to their baseline counterparts.

Losers and prospectors in the short‐term options market

AbstractIntraday data for weekly options are investigated for behavioral biases implied in prospect theory (PT) and cumulative prospect theory (CPT). The results generally support both theories, with losers (winners) observed to be relatively risk‐seeking (averse). On aggregate, losers (winners) overprice (underprice) their contracts and overweight (underweight) the probability of winning. Additionally, the volatility smirk observed in equity options is dampened by PT/CPT biases. The price distortions are time sensitive, especially for losing traders. Losers hold out by transacting later in the day and closer to expiration than their baseline counterparts. This betting‐time effect is absent among winners.

Influence of social conversational features on language identification in highly multilingual online conversations

With the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important task for various applications. While automatic language identification (ALI) in social media text is considered to be a non-trivial task due to the presence of slang words, misspellings, creative spellings and special elements such as hashtags, user mentions etc., ALI in multilingual environment becomes even more challenging task. In a highly multilingual society, code-mixing without affecting the underlying language sense has become a natural phenomenon. In such a dynamic environment, conversational text alone often fails to identify the underlying languages present in the text. This paper proposes various methods of exploiting social conversational features for enhancing ALI performance. Although social conversational features for ALI have been explored previously using methods like probabilistic language modeling, these models often fail to address issues related to code-mixing, phonetic typing, out-of-vocabulary etc. which are prevalent in a highly multilingual environment. This paper differs in the way the social conversational features are used to propose text refinement strategies that are suitable for ALI in highly multilingual environment. The contributions in this paper therefore includes the following. First, this paper analyzes the characteristics of various social conversational features by exploiting language usage patterns. Second, various methods of text refinement suitable for language identification are proposed. Third, the effects of the proposed refinement methods are investigated using various sentence level language identification frameworks. From various experimental observations over three conversational datasets collected from Facebook, Youtube and Twitter social media platforms, it is evident that our proposed method of ALI using social conversational features outperforms the baseline counterparts.

Fabricating structural adhesive bonds with high electrical conductivity

In this paper we present a new approach, called Two-Step-Functionalization approach to integrate electric conductivity function into structural adhesive bonds. A nylon textile is used for supporting polymer adhesive film and it is especially surface-coated with silver (Ag) to produce an electrically conductive adhesive film. This is the first-step-functionalization and it occurs at micro-scale. The functionalized film is then surface-loaded on the both sides with three-dimensional tetrapod-like zinc oxide (TZnOs) particles coated again with Ag to form a sandwich structure, where the both thin surface layers are TZnO-rich. The sandwich film is then positioned between two aluminum adherends and bonded at the curing condition. In the context, this is the second-step-functionalization, and this occurs at nano-scale. Obviously, both the functionalization provides not only significant mechanical reinforcing efficiency, but also electrical connection between the bonded joints. Eventual electrical isolation effect is avoided which often occurs if the interface between the adhesive film and metal adherend is wetted by the adhesive in a low viscosity window during the curing process. As demonstrated, the adhesive bonded joints using a very limited amount of Ag as coating material shows a unique high electrical conductivity and better mechanical properties as compared to baseline counterparts.

Fungal infection counters insecticide resistance in African malaria mosquitoes

The evolution of insecticide resistance in mosquitoes is threatening the effectiveness and sustainability of malaria control programs in various parts of the world. Through their unique mode of action, entomopathogenic fungi provide promising alternatives to chemical control. However, potential interactions between fungal infection and insecticide resistance, such as cross-resistance, have not been investigated. We show that insecticide-resistant Anopheles mosquitoes remain susceptible to infection with the fungus Beauveria bassiana. Four different mosquito strains with high resistance levels against pyrethroids, organochlorines, or carbamates were equally susceptible to B. bassiana infection as their baseline counterparts, showing significantly reduced mosquito survival. Moreover, fungal infection reduced the expression of resistance to the key public health insecticides permethrin and dichlorodiphenyltrichloroethane. Mosquitoes preinfected with B. bassiana or Metarhizium anisopliae showed a significant increase in mortality after insecticide exposure compared with uninfected control mosquitoes. Our results show a high potential utility of fungal biopesticides for complementing existing vector control measures and provide products for use in resistance management strategies.